There is currently a growing new focus in data mining – Ubiquitous Data Mining (UDM). UDM is the process of mining data streams in a ubiquitous environment, on resource constrained devices [KPP02]. UDM is widely applied in facilitating real-time decision making in mobile and highly dynamic environments/applications, such as road safety and mobile stock portfolio monitoring. A significant challenge in these contexts is the interpretation and analysis of results produced through unsupervised techniques (which are invaluable since little is known about the streamed data). We propose a novel fuzzy approach that leverages the significant benefits of UDM clustering and supplements the interpretation and use of these results through using expert/background knowledge.